The terms, ‘text mining’ or ‘text data mining’, generally relate to performing analysis of vast quantities of textual content with an aim to derive insights from the data contained therein. Examples of textual content may include word documents, emails, instant messenger conversations, web textual content like blogs, online articles, user posts on social networking websites, and the like. In an example scenario, text mining techniques may be applied to textual content associated with dialogues, such as online chat conversations (or even text transcripts of voice call conversations) between customer service representatives and potential/existing customers. The application of the text mining techniques to dialogues may be performed to gain insights into the possible intentions of the customers for contacting the customer service representatives and also into the possible recommendations that may be offered to the customers.
Typical text mining techniques are not configured to differentiate among expository, descriptive or narrative textual content and goal-directed dialogues such as conversations between customer service representatives and customers. Further, such techniques may extract features from the conversations without attempting to exploit any inherent structure arising from nature and purpose of the conversations. As a result, conventional techniques for text mining are rendered inadequate for the purpose of deriving insights from textual content, such as dialogues.